Abstract

Demand for different modes of transportation clearly interacts. If public transit is delayed or out of service, customers might use mobility on demand (MoD), including taxi and carsharing for their trip, or discard the trip altogether, including a first and last mile that might otherwise be covered by MoD. For operators of taxi and carsharing services, as well as dispatching agencies, understanding increasing demand, and changing demand patterns due to outages and delays is important, as a more precise demand prediction allows for them to more profitably operate. For public authorities, it is paramount to understand this interaction when regulating transportation services. We investigate the interaction between public transit delays and demand for carsharing and taxi, as measured by the fraction of demand variance that can be explained by delays and the changing OD-patterns. A descriptive analysis of the public transit data set yields that delays and MoD demand both highly depend on the weekday and time of day, as well as the location within the city, and that delays in the city and in consecutive time intervals are correlated. Thus, demand variations must by corrected for these external influences. We find that demand for taxi and carsharing increases if the delay of public transit increases and this effect is stronger for taxi. Delays can explain at least 4.1% (carsharing) and 18.8% (taxi) of the demand variance, which is a good result when considering that other influencing factors, such as time of day or weather exert stronger influences. Further, planned public transit outages significantly change OD-patterns of taxi and carsharing.

Highlights

  • Metropolitan areas suffer as a consequence of a car-centric city layout

  • We give a high level relation, calculate the fraction of demand variation that can be explained by public transit delays, analyze the varying demand patterns during outages, the probability of having no vehicles available depending on delays and outages, and the demand changes during the COVID-19 pandemic

  • If all delays were reduced by 50%, the average demand for taxi would decrease by 1.6%, and the average demand for carsharing would decrease by 0.5%

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Summary

Introduction

Metropolitan areas suffer as a consequence of a car-centric city layout. Roads are frequently congested [1], air quality decreases [2], and valuable space for active mobility is restricted [3]. Surveys indicate that users switch to road-based individual transportation if delays increase or public transit is unavailable [9], resulting in additional demand. This additional demand (i) increases road congestion and (ii) results in additional planning effort for MoD operators. It helps MoD operators to increase the quality of service by improving their demand predictions and, consequentially, their operational strategies (e.g., [12]), and gives public transit authorities first insights into how their delays impact road traffic, and eventually congestion.

Related Work
Reference Datasets
Public Transit Data
Carsharing Data
Taxi Data
Data Discretization and Filtering
Censored Demand
Analysis
High Level Relation
Explained Demand Variance Due to Delays
Changing Demand Patterns in Presence of Known Outages
Decrease of Demand along Lines during COVID-19
Potential for Fleet Size Reduction
Findings
Discussion and Conclusions
Full Text
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